Real-time per-frame camera pose estimation is a core problem in the Simultaneous Localization and Mapping (SLAM) literature. Several real-time monocular RGB methods (e.g., [1]}, [2]}, [3]}, [4]}) typically
rely on either pose-graph optimization [5]}
or bundle adjustment [6]}.
In the case of dense reconstruction, MonoFusion [7]} integrates sparse SLAM bundle adjustment with dense volumetric fusion. DTAM [8]} estimates camera poses directly from the reconstructed dense 3D model based on frame-to-model tracking.
Based on GPU optimization techniques, several methods [9]}, [10]} realizes real-time global pose alignment.
